Cloudera

MAY 14, 2024

To attain that level of data quality, a majority of business and IT leaders have opted to take a hybrid approach to data management, moving data between cloud, on-premises -or a combination of the two – to where they can best use it for analytics or feeding AI models. What do we mean by ‘true’ hybrid? Let’s dive deeper.

Cloud 99

Cloud Data Governance Unstructured Data Data Architecture 99

Cloudera

JUNE 7, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

IT 112

IT Unstructured Data Data Architecture Government 112

Snowflake

NOVEMBER 2, 2023

And, since historically tools and commercial platforms were often designed to align with one specific architecture pattern, organizations struggled to adapt to changing business needs – which of course has implications on data architecture. The schema of semi-structured data tends to evolve over time.

Data Lake 115

Data Lake Data Warehouse Cloud Unstructured Data 115

Striim

SEPTEMBER 11, 2024

Data pipelines are the backbone of your business’s data architecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. Understanding the essential components of data pipelines is crucial for designing efficient and effective data architectures.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Data Engineering Podcast

DECEMBER 8, 2019

Links SnowflakeDB Free Trial Stack Overflow Data Warehouse Oracle DB MPP == Massively Parallel Processing Shared Nothing Architecture Multi-Cluster Shared Data Architecture Google BigQuery AWS Redshift AWS Redshift Spectrum Presto Podcast Episode SnowflakeDB Semi-Structured Data Types Hive ACID == Atomicity, Consistency, Isolation, Durability 3rd Normal (..)

Data Warehouse 100

Data Warehouse Cloud AWS Relational Database 100

Cloudera

SEPTEMBER 12, 2022

First, organizations have a tough time getting their arms around their data. More data is generated in ever wider varieties and in ever more locations. Organizations don’t know what they have anymore and so can’t fully capitalize on it — the majority of data generated goes unused in decision making. Unified data fabric.

Unstructured Data 75

Unstructured Data Data Lake Data Architecture Data 75

Towards Data Science

FEBRUARY 15, 2023

In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture. The proposal is simple — “Trow everything you have here inside and worry later”.

Data Lake 98

Data Lake Data Warehouse Hadoop Architecture 98

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Architecture 124

Architecture Metadata Kafka Government 124

Edureka

APRIL 14, 2025

Meanwhile, the visualization tool offers wide-ranging data connectors—from Azure SQL and SharePoint to Salesforce and Google Analytics—enabling quick access to structured and semi-structured data. However, it leans more toward transforming and presenting cleaned data rather than processing raw datasets.

BI 40

BI Business Intelligence Raw Data Retail 40

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

ProjectPro

JUNE 6, 2015

At ProjectPro we had the pleasure to invite Abed Ajraou , the Director of the BI & Big Data in Solocal Group (Yellow Pages in France) to speak about the digital transformation from BI to Big Data. The goal of BI is to create intelligence through Data. The goal of BI is to create intelligence through Data.

Business Intelligence 40

Business Intelligence Big Data BI Hadoop 40

Knowledge Hut

SEPTEMBER 26, 2023

It leverages a Massively Parallel Processing (MPP) architecture, which is optimized for executing complex analytical queries on large datasets efficiently. This makes it an excellent choice for organizations that need to analyze large volumes of structured and semi-structured data quickly and effectively.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Monte Carlo

APRIL 1, 2021

Unstructured data is problematic as it relates to data catalogs because it’s not organized, and if it is, it’s often not declared as organized. While modern data architectures, including data lakes, are often distributed, data catalogs are usually not, treating data like a one-dimensional entity.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Government 52

Knowledge Hut

NOVEMBER 28, 2023

As a result, most companies are transforming into data-driven organizations harnessing the power of big data. Here Data Science becomes relevant as it deals with converting unstructured and messy data into structured data sets for actionable business insights.

Data Architect 52

Data Architect Data Science Certification Big Data 52

Knowledge Hut

OCTOBER 30, 2023

Data Transformation and ETL: Handle more complex data transformation and ETL (Extract, Transform, Load) processes, including handling data from multiple sources and dealing with complex data structures. Ensure compliance with data protection regulations. Define data architecture standards and best practices.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

Ascend.io

JULY 17, 2023

If you work with data, you’ll have come across the term data mesh by now. This decentralized but interconnected approach to structuring data has become increasingly popular since the term was coined by Zhamak Dehghani 4 years ago. Essentially, you’re risking scaling up your problems along with your data architecture.

Architecture 52

Architecture Telecommunication Datasets Data Pipeline 52

Monte Carlo

AUGUST 31, 2023

We’ll take a closer look at variables that can impact your data next. Migration to the cloud Twenty years ago, your data warehouse (a place to transform and store structured data) probably would have lived in an office basement, not on AWS or Azure. What is a decentralized data architecture?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Silectis

JANUARY 3, 2021

Big Data Processing In order to extract value or insights out of big data, one must first process it using big data processing software or frameworks, such as Hadoop. Big Query Google’s cloud data warehouse. Data Catalog An organized inventory of data assets relying on metadata to help with data management.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Ascend.io

FEBRUARY 28, 2024

Read More: AI Data Platform: Key Requirements for Fueling AI Initiatives How Data Engineering Enables AI Data engineering is the backbone of AI’s potential to transform industries , offering the essential infrastructure that powers AI algorithms.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

U-Next

AUGUST 26, 2022

Binarized Search Trees: Simple data architectures are grid search trees. They enable numerous solutions for a certain issue, letting the user choose the optimal data structure to address the issue. The Benefits Of Data Structures. Utilizing a data structure makes retrieving data from a storage source easier.

Algorithm 40

Algorithm Data Utilities Data Science 40

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake Machine Learning BI 64

Rockset

AUGUST 2, 2023

The vessel positions data which in nature is a time series geospatial data set, was stored in both PostgreSQL and Cassandra to be able to support different use cases. Furthermore, as Windward introduced new use cases they started to hit limitations with their data stack.

Database-centric 52

Database-centric PostgreSQL Transportation Insurance 52

AltexSoft

AUGUST 29, 2023

Also, data lakes support ELT (Extract, Load, Transform) processes, in which transformation can happen after the data is loaded in a centralized store. A data lakehouse may be an option if you want the best of both worlds. Data sources can be broadly classified into three categories. Structured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Knowledge Hut

JANUARY 18, 2024

4 Purpose Utilize the derived findings and insights to make informed decisions The purpose of AI is to provide software capable enough to reason on the input provided and explain the output 5 Types of Data Different types of data can be used as input for the Data Science lifecycle.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

Towards Data Science

AUGUST 21, 2024

Getting to the bottom of what structuring your data responsibly really means Continue reading on Towards Data Science »

Data Science 75

Data Science Data Structured Data Data Architecture 75

Knowledge Hut

NOVEMBER 28, 2023

The data goes through various stages, such as cleansing, processing, warehousing, and some other processes, before the data scientists start analyzing the data they have garnered. The data analysis stage is important as the data scientists extract value and knowledge from the processed, structured data.

Data Science 52

Data Science Telecommunication Recruitment Finance 52

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

AltexSoft

OCTOBER 25, 2021

But a lot of data (by different estimations, 70 or 80 percent of all clinical data) remains unstructured , kept in textual reports, clinical notes, observations, and other narrative text. Unstructured data is unavoidable, yet extremely valuable. However useful, CDSSs are mostly limited to processing only structured data.

Medical 52

Medical Healthcare Process Hospitality 52

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Traditional data warehouse platform architecture. Unstructured and streaming data support.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Rockset

JANUARY 28, 2020

Testing new functionality on their transactional data store is costly and can impact production. Ad hoc queries to measure the accuracy of the checkout process in real time are not possible with traditional data architectures. Standard productionizes several endpoints a day using this methodology.

Retail 40

Retail Google Cloud Raw Data SQL 40

AltexSoft

MARCH 2, 2023

Data modeling involves creating a conceptual representation of data objects and their relationships to each other, as well as the rules governing those relationships. To design an effective data governance program, it’s crucial to choose an operational model that fits your business size and structure.

Data Governance 52

Data Governance Government Programming Healthcare 52

Edureka

JULY 4, 2024

By letting you query data directly in the lake without the need for movement, Synapse cuts down the storage costs and eliminates data duplication. This capability fosters a more flexible data architecture where data can be processed and analyzed in its raw form.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

AltexSoft

JULY 19, 2022

What data mesh is and is not. What data mesh IS. Data mesh is a set of principles for designing a modern distributed data architecture that focuses on business domains, not the technology used, and treats data as a product. For example, your organization has an HR platform that produces employee data.

Architecture 52

Architecture Data Lake Medical Datasets 52

ProjectPro

FEBRUARY 16, 2023

The SQL-on-Hadoop platform combines the Hadoop data architecture with traditional SQL-style structured data querying to create a specific analytical application tool. Data engineers can extract data from the Hadoop system using Hive and Impala , which offer an SQL-like interface.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

ProjectPro

JANUARY 31, 2022

Snowflake in Action at Western Union Snowflake's multi-cluster shared data architecture expanded instantaneously to serve Western Union's data, users, and workloads without causing resource conflict. Snowflake saves and manages data on the cloud using a shared-disk approach, making data management simple.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

Monte Carlo

JUNE 14, 2023

Does data quality need to be high will directionally accurate suffice? Let the data drive the data pipeline architecture. Most data teams will be handling mostly structured data for analytical purposes making a data warehouse based data pipeline architecture a natural fit.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Rockset

FEBRUARY 24, 2023

Introduction Let’s get this out of the way at the beginning: understanding effective streaming data architectures is hard, and understanding how to make use of streaming data for analytics is really hard. Kafka or Kinesis ? Stream processing or an OLAP database? Open source or fully managed?

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52